def apply_gradients()

in safe_rl/utils/mpi_tf.py [0:0]

• 5 lines of code
• 2 McCabe index (conditional complexity)

    def apply_gradients(self, grads_and_vars, global_step=None, name=None):
        """
        Same as normal apply_gradients, except sync params after update.
        """
        opt = super().apply_gradients(grads_and_vars, global_step, name)
        with tf.control_dependencies([opt]):
            sync = sync_params([v for g,v in grads_and_vars])
        return tf.group([opt, sync])